Desperate times bring desperate measures

Update: Edwardo Porter makes the same point, only he does a much better job of it.

A bit off topic, but the impending government shutdown has me thinking in simple game theoretic terms.

Some on the left (and right) seem to think that Congressional actions are "crazy" as government shutdown is likely to hurt the Republican party.  After all, that's what happened the last time when Newt Gingrich shut down the government in 1995, which led to his demise and helped Clinton win reelection against Dole in 1996.

It's probably fair to guess that, while this time is different (isn't every time, at least a little?), the shutdown will likely hurt the Republican party.  So why are they doing it?  Are they really crazy?  Has the radical fringe taken over and leading us over the cliff to disaster?

Well maybe. But maybe their actions, even if potentially disastrous, are rational and not surprising given the circumstances.  It seems to me the Republican party is in a desperate situation, and desperate times rationally bring about desperate actions. It's possible, though probably unlikely, that Obama and the Democrats will cave and give Republicans something in exchange, like partial repeal of the health care law, for not blowing up the economy.  It also seems possible, though unlikely, that shutdown and/or default will hurt Democrats as much or more than Republicans.  Even if these are unlikely propositions, they have more than zero probability.

The alternative is that Republicans do nothing and let Obamacare be implemented, the economy continues to recover, and the nation's demographics steadily change, all of which basically ensures death of the modern Republican party. So, do they go for the Hail Mary pass or just give up?  It seems to me that a rational party goes for the Hail Mary pass, which is what they're doing.

So, the good news is that the Republican party, Tea Partiers included, probably isn't crazy.  The bad news is that it's hard to see how this whole thing plays out without the country, and possibly much of the world, being badly hurt.

Climate Change and Resource Rents

With the next IPCC report coming out, there's been more reporting on climate change issues.  Brad Plumer over a Wonkblog has nice summary that helps to illustrate how much climate change is already "baked in" so to speak.

I'd like to comment one point.  Brad writes "Humans can only burn about one-sixth of their fossil fuel reserves if they want to keep global warming below 2ºC."

I'd guess some might quibble with the measurement a bit, since viable reserves depends on price and technology, plus many unknowns about much fossil fuel there really is down there.  But this is probably in the ballpark, and possibly conservative.

Now imagine you own a lot of oil, coal and/or natural gas, you're reading Brad Plumber, and wondering what might happen to climate policy in the coming years.  Maybe not next year or even in the next five or ten years, but you might expect that eventually governments will start doing a lot more to curb fossil fuel use.  You might then want to sell your fossil fuels now or very soon, while you can.   If many resource owners feel this way, fossil fuel prices could fall and CO2 emissions would increase.  

This observation amounts to the so-called "green paradox."  Related arguments suggest that taxing carbon may have little influence on use, and subsidizing renewable fuels and alternative technologies, without taxing or otherwise limiting carbon-based fuels, might make global warming worse, since it could push emissions toward the present.

Research on these ideas, mostly theoretical, is pretty hot in environmental economics right now.  It seems like half the submissions I manage at JEEM touch on the green paradox in one way or another.  

All of it has me thinking about a point my advisor Peter Berck often made when I was in grad school. At the time, we were puzzling over different reasons why prices for non-renewable resources--mostly metals and fossil fuels--were not trending up like Hotelling's rule says they should.  Peter suggested that we may never use the resources up, because if we did, we'd choke on all the pollution.  Resource use would effectively be banned before all of it could be used. If resource owners recognized this, they'd have no incentive to hold or store natural resources and the resource rent (basically the intrinsic value based on its finite supply) would be zero, which could help explain non-increasing resource prices.

For all practical purposes, Peter understood the green paradox some 15-20 years ago.  Now the literature is finally playing catch up.  

I've been touched by genius

Awesome news.  My colleague David Lobell just won the MacArthur grant.

http://news.stanford.edu/news/2013/september/macarthur-fellowship-awards-092513.html

Seriously, David is a fantastic colleague and very deserving of this award.  Also, I think we have some great new research in the pipeline and with any luck this might help bring some exposure to it.

GGG is among the top 200 most influential economics blogs (just barely)

I just stumbled upon this ranking via Econobrowser, which is number 10, and one of the blogs I really like to visit.

Greed, Green and Grains is number 199.

Well, I guess that's not a crown jewel, but I'll take it, especially given how my little niche isn't one of the biggest fields of economics and how little time I have to dedicate to this thing. 

I realize posting is thin.  I will try to post when I can, but my commitments are just too many to post much these days.  G-FEED, which is steadily growing in influence, will have more posts because there a number of us contributing, some of whom are rapidly becoming the rock stars of science, with some major publications and media attention.

Crop insurance under climate change

How should crop insurance premiums adjust to a changing climate in order to remain actuarial fair?
Short answer:  Very slowly.

That seems pretty obvious to me, and hopefully to anyone who thinks about it for a few minutes, even if you think climate change is ultimately going to have big impacts.  Moreover, the way crop insurance premiums are already determined---as a function a farmer's own recent yield history---gradual adjustment of premiums will take place naturally.

So, what should USDA's Risk Management Agency do, if we think nasty crop outcomes like last year are going to be more frequent going forward?

Well, I'll abstain from making a recommendation, but I will say that if they do absolutely nothing, there will be no significant budgetary implications.

None of this is to say that there might not be other ways to improve crop insurance.

Update: So, if this issue is so unimportant, why do I mention it?  Because I'm seeing and hearing the question a lot, and my general sense is that energy and resources might be better spent on other issues.

Integrated assessment models: What do they tell us about climate change policy?

"Very little,"  according to Robert Pindyck in a new working paper.

Integrated assessment models (IAMs to practitioners) stitch together projections from climate models, energy sector models, agronomic crop models, models of other sectors of the economy, and partial or general equilibrium models that account for price and interactions with the broader economy to derive a more comprehensive evaluation of costs and benefits from climate change.

Pindyck is understandably frustrated with the false sense of precision these models can impart.  As he explains, a few reasonable tweaks of any of these models can give very different estimates about the social cost of carbon---the price we should pay, but typically don't, for emitting CO2.

Pindyck raises some good criticisms about IAMs, or at least says out loud a lot of things that many economists have quietly said to each other.  I'm glad he's bringing our varying assumptions and wildly varying cost-of-carbon estimates out into the open for all to see.  Perhaps it will push us to make our modeling efforts a little more useful, or at least more transparent.

He's right to pick on false precision.  But I wonder: has anyone really been fooled?  My sense is no.  One positive thing about these modeling efforts is that they allow us to see which assumptions are most critical. They are nice (black?) boxes for testing out the sensitivity of X on overall climate impacts. This might help us frame more reasonable discussion about the possibilities and what we should do.  It might also help researchers focus future empirical efforts.

The extreme sensitivity of results to seemingly innocuous assumptions also shows how uncertain the impacts of climate change really are.  Indeed, not long ago Pindyck published a paper in JEEM with results that are extremely sensitive to his assumption that the world will end in 500 to 1000 years (an assumption that could be more transparent--see his footnote #13), among others.

So let's take our IAMs with salt, and encourage developers of the models to be as transparent as possible about their assumptions and how and why their models differ from each other.  But let's also not forget that they have a place in this business, albeit perhaps a bit less than IAM builders might have you believe.

Schneider and Schneider and Lane also have nice critiques of IAMs.

GMOs: Franken food or technological savior?

Amy Harmon has a great in-depth story in the New York Times about the science and controversy surrounding GMO crops.  She builds the article around the worldwide problem of citrus greening, but nicely builds in abroader story about GMOs in general.

Another great source for learning more about the GMO controversy is the book Tomorrow's Table, by Pamela Ronald and Raoul Adamchak.

My own take on GMOs so far: The hysteria against them is likely overblown, but the extraordinary promises by technological optimists are overblown too.  Traditional breeding is a solid and, over the long run, often superior and less costly substitute to GMOs.  What's more worrisome to me is that intellectual property laws and regulatory costs may be acting to concentrate the seed business and make it less competitive.  These later issues are complex, not exactly my forte, and I don't presently see clear answers to any of it.

Anyhow, it's nice to see good reporting on an evocative topic.

Commodity Speculation or Market Power?

After seeing how much Goldman profited from selling MBS that they knew were junk, it's hard to feel sorry for Goldman receiving so much grief for its commodity storage and trading activities.  The worry seems to be that because Goldman has become increasingly involved in commodities markets that they must be manipulating prices for profit, and in the process pushing prices away from their fundamental values---ie., supply and demand.

Do we actually know whether there is a problem here? It's possible that Wall Street is trying to manipulate the market.  But this is a hard thing to do, even for a really big company, especially one that doesn't produce the stuff it's trying to monopolize.  Also bear in mind that anyone can buy and store commodities, so it's not like there are huge barriers to entry.  Those who have tried to corner commodity markets in the past haven't fared well.

My sense is that cornering a commodity market via hoarding is basically impossible once the market realizes what the major player(s) is doing.  And if they're having senate hearings about Goldman's storage and trading activities, I think it's fair to say the cat's out of the bag.

So, what is Goldman doing? If it's not a market power story I'd guess they're trying to buy low and sell high, just like everybody else. They probably believe they have a better handle on market fundamentals than other commodity speculators.  Perhaps they do.  But if this is all they are doing, then they are effectively reducing price volatility and helping to make the market work more efficiently.

On public radio this morning a reporter (sorry, I forget who), asked Omarova whether Goldman's profits just meant that consumers were paying higher prices.  Omarova said "that's absolutely right." But it's absolutely wrong if Goldman's just speculating.  Goldman's profits are coming out of the pockets of speculators who bet prices would fall when they rose, and vice versa.  In fact, that's probably the case if it's a market power issue too.

Anyway, if this is about Goldman trying to corner the storage market, that's a problem and Goldman deserves the grief they're receiving. But that strikes me as unlikely as it would be foolhardy.  My guess is that this is just speculation, which means Goldman's profits translate directly to better allocation of commodities over time, less commodity price volatility, and basically zero influence on average prices.

The Farm Bill, a.k.a Hunger Games

At this point in our broader political discourse, I probably shouldn't surprised about the House's vote on the farm bill, which continues generous support for wealthy farmers and eliminates food stamps. 

I'm trying to keep my blogging more positive and analytical than normative.  I think the analysis of this is pretty clear, so not much to say here that others, like Paul Krugman, have already done much better than I can. (Incidentally, the 1400+ comments on that article, many of which look very thoughtful, looks like a record to my recollection).

One thing I might add:  In my career studying agricultural policy in the US, I have heard many, many economists of all political stripes lambast our agricultural subsidies.  Greg Mankiw pointed to them as one of the key areas where most economists generally agree.  But I rarely hear economists of any political stripe criticize our food stamp program.  About the harshest economic criticism I've seen as that we should give people cash rather than food stamps.

We live in truly bizarre times.

Macro, Multipliers and the Environment

A little follow up from my post the other day:  It's probably going too far to say investment to curb climate change, if made during a depression, is a free lunch.  But certainly the basic benefit-cost analysis for what constitutes the most efficient policy with respect to climate change, or any other environmental or public good, changes when there is another massive market failure at play.  Spending to reduce emissions would seem to have two benefits: reduced externalities plus closing the macro output gap.

In some ways it feels a little like the so-called "double-dividend" hypothesis: the idea that taxing pollution can solve the environmental externality while raising revenue that can reduce distortionary income or sales taxes.  That rather compelling idea still gets kicked around a lot, and there is probably a small truth to it, although the calculation turns out to be more subtle (see Goulder's review, for example).

At first blush, the macro double dividend seems like it could be much larger.  As the late James Tobin apparently used to say, it takes a lot of Harberger triangles to fill and Okun gap.  The old double dividend literature dabbles with the former, and now we're talking about the latter.  I'm not familiar enough with the literature to know whether there have been attempts to bridge these vastly different areas of economics.  It strikes me as a difficult thing to do.  And even if it were done well, likely hard to publish due to the macro wars.

Still, if environmental policy were to be structured with macro multipliers in mind, it could change the entire calculus about the relative benefits of standards versus prices, especially if one would induce more spending in the near term.  It might also alter the implications of uncertainty.  Standard micro analysis, which is fashionable in environmental economics, favors delayed timing of investments, but with small economic values at stake.  The macro effect would strongly favor investment now, with presumably big economic stakes.

Of course, there are public goods besides reducing environmental externalities.  Spending on basic infrastructure like roads, bridges, tunnels and railways might have similar double dividends.  So how do we more generally evaluate the costs and benefits of public policies in a depressed economy, assuming (as I would) that macro output gaps are real may be with us for awhile?

I don't know the answer to this question.  But there would seem to be a lot more to it than measuring multipliers.  So, who are the brave, inquisitive souls willing to dive in?

Taking action on climate change

Update: Krugman expanded on the job creation point in his column.

The Obama administration is side stepping congress and finally doing something about climate change.  The "action plan" has a nice outline of strategies, but no specifics.  It will be interesting to see what kinds of rules the EPA and DOE roll out in response to this initiative and how they will be justified under existing laws like the Clear Air Act.

Precedent for this kind of action was established by the Supreme Court awhile back.  If the Obama administration didn't take action soon, agencies would be sued by environmental groups and forced to do something.  So this kind of thing was bound to happen, one way or another.

In response, Paul Krugman makes an interesting and surely controversial point.  The new rules, whatever they turn out to be, will make energy more costly.  That's not to say action shouldn't be taken, but that there are tradeoffs involved with curbing climate change.  Krugman argues, however, that because these are not ordinary times, the costs may be considerably less.  Indeed, these rules may actually benefit the rest of the economy, not hurt it.  In other words, action on climate change could be free lunch.

Yes, this violates one of the first principles of economics.  But that sort of thing might actually happen when we have a depressed economy and vast inefficiency to begin with.  His reasoning is that we have too little demand right now, so that investments into alternative energy or carbon capture would employ resources that would otherwise sit idle.  And once those idle resources are employed, the economic activity they would generate would grow real income.  Put another way, since aggregate demand is insufficient, investments to curb global warming do not displace other kinds of investments and instead just add to GDP.

Environmental economists don't think this way, probably because depressed economies don't happen very often, and so the field pays little attention to macroeconomics.  But since the economy is depressed and likely to stay that way for at least another year or two, it does seem like a good time take action. After all, as Krugman likes to remind us again and again,  the latest evidence shows Keynesian ideas to be stronger than ever.  Let's eat that free lunch while we can.

Do journal impact factors distort science?

From my inbox:

An ad hoc coalition of unlikely insurgents -- scientists, journal editors and publishers, scholarly societies, and research funders across many scientific disciplines -- today posted an international declaration calling on the world scientific community to eliminate the role of the journal impact factor (JIF) in evaluating research for funding, hiring, promotion, or institutional effectiveness.

Here's the rest of the story at Science Daily:

And a link to DORA, the "ad hoc coalition" in question.

It seems fairly obvious that impact factors do distort science.  But I wonder how much, and I also wonder if there are realistic alternatives that would do a better job of encouraging good science.

There are delicate tradeoffs here: some literatures seem to become mired within their own dark corners, forming small circles of scholars that speak a common language.  They review each others' work, sometimes because no one else can understand it, or sometimes because no one else cares to understand it.  The circle has high regard for itself, but the work is pointless to those residing outside of it.

At the same time, people obviously have very different ideas about what constitutes good science.

So, what does the right model for evaluating science look like?

Consensus Statements on Sea Level Rise

In my mailbox from the AGU:

After four days of scientific presentations about the state of knowledge on sea-level rise, the participants reached agreement on a number of important key statements. These statements are the reflection of the participants of the conference and not official positions from the sponsoring societies.

 

Earth scientists agree that the global sea level is rising at an accelerated rate overall in response to climate change.

Scientists have a professional responsibility to inform government, the public, and the private sector about the impacts of rising sea levels and extreme events, and the risks they pose.

 

The geological record indicates that the current rates of sea-level rise in many regions are unprecedented relative to rates of the last several thousand years.

Global sea-level rise has changed rapidly in the past and scientific projections show it will continue to rise over the course of this century, altering our coasts.

 

Extreme events and their associated impacts will be more damaging and pose higher risks in the immediate future than sea-level rise.

Increasing human activity, such as land use change and water management practices, adds stress to already fragile ecosystems and can affect coasts just as much as sea-level rise.

 

Sea-level rise will exacerbate the impacts of extreme events, such as hurricanes and storms, over the long-term.

Extreme events have contributed to loss of life, billions of dollars in damage to infrastructure, massive taxpayer funding for recovery, and degradation of our ecosystems.

 

In order to secure a sustainable future, society must learn to anticipate, live with, and adapt to the dynamics of a rapidly evolving coastal system.

Over time feasible choices may change as rising sea level limits certain options. Weighing the best decisions will require the sharing of scientific information, the coordination of policies and actions, and adaptive management approaches.

 

Well-informed policy decisions are imperative and should be based upon the best available science, recognizing the need for involvement of key stakeholders and relevant experts.

As we work to adapt to accelerating sea level rise, deep reductions in emissions remain one of the best ways to limit the magnitude and pace of rising seas and cut the costs of adaptation.

 

Spatial Econometric Peeves (wonkish)

Nearly all observational data show strong spatial patterns.  Location matters, partly due to geophysical attributes, partly because of history, and partly because all the things that follow from these two key factors tend to feedback and exaggerate spatial patterns.  If you're a data monkey you probably like to look at cool maps that illustrate spatial patterns, and spend a lot of time trying to make sense of them.  I know I do.

Most observational empirical studies in economics and other disciplines need to account for this general spatial connectedness of things.  In principal, you can do this two ways:  (1) develop a model of the spatial relationship; (2) account for the spatial connectedness by appropriately adjusting the standard errors of your regression model.

The first option is a truly heroic one, and most all attempts I've seen seem foolhardy.  Spatial geographic patters are extremely complex and follow from deep geophysical and social histories (read Guns, Germs and Steal). One is unlikely to uncover the full mechanism that underlies the spatial pattern.  When one "models" this spatial pattern, assumptions drive the result, and the assumptions are, almost always, a heroic leap of faith.

That leaves (2), which shouldn't be all that difficult using modern statistical techniques, but does take some care and perhaps a little experimentation.  It seems to me many are a little too blithe about it, and perhaps select methods that falsely exaggerate statistical significance.

Essentially, the problem is that there's normally a lot less information in a large data set than you think, because most observations from a region and/or time are correlated with other observations from that region and/or time.  In statistical speak, the errors are clustered.

To illustrate how much this matters, I'll share some preliminary regressions from a current project of mine.  Here I am predicting the natural log of corn yield using field-level data that span about 15 years on most of the corn fields in three major corn-producing U.S. states. I've got several hundred thousand observations. Yes, you read that right--it's a very rich data set.

But corn yields, as you can probably guess, tend to have a lot of spatial correlation. This happens in large part because weather, soils, and farming practices are spatially correlated.  However, there isn't a lot of serial correlation in weather from year to year, so, my data are highly correlated within years, and average outcomes have strong geographic correlation, but errors are mostly independent between years in a fixed location.

Where the amount of information in the data normally scales with the square root of the sample size, when the data are clustered spatially or otherwise, a conservative estimate for the amount of information is the square root of the number of clusters you have.  In this data set, we don't really have fixed clusters.  It's more like smooth overlapping clusters.  But we might proxy the "number" of clusters  around the square root of 45, the number of years X states I have, because most spatial correlation in weather fades out after about 500 miles.  Although these states border each other, so it may be even less than 45.  Now, I do have weather matched to each field depending on the field's individual planting date, which can vary a fair amount.  That adds some statistical power. So, I hope it's a bit better than the square root of 45.  Either way, in the ballpark of 45 is a whole lot less than several hundred thousand.

I regress the natural log of corn yield on

YEAR:             a time trend
log (potential):  (output of a crop model calibrated from daily weather inputs),
gdd:                  growing degree days (a temperature measure),
DD29:              degree days above 29C (a preferred measure of extreme heat),
prec & prec^2:  season precipitation and precipitation squared,
PDay:                number of days since Jan 1 until planting
interaction between DD29 and CO2 exposure.

CO2 exposure varies a little bit spatially, and also temporally, both due to a trend from burning fossil fuels and other emissions, as well as seasonal fluctuations following from tree and leaf growth (earlier planting tends to have higher CO2, and higher CO2 can improve radiation water use efficiency in corn, which can effectively make the plants more drought tolerant).

The standard regression output gives:

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     2.320e+00  3.014e-02   76.98   2e-16
I(YEAR - 2000)  1.291e-02  4.600e-04   28.06   2e-16
log(Potential)  5.697e-01  5.470e-03  104.14   2e-16
gdd             1.931e-04  4.177e-06   46.24   2e-16
DD29           -2.477e-02  1.149e-03  -21.56   2e-16
Prec            1.787e-02  9.424e-04   18.96   2e-16
I(Prec^2)      -4.939e-04  2.038e-05  -24.24   2e-16
PDay           -6.798e-03  6.269e-05 -108.45   2e-16
DD29:AvgCO2     6.229e-05  2.953e-06   21.09   2e-16

Notice the huge t-statistics: all the parameters look precisely identified.  But you should be skeptical.

Most people now use White "robust" standard errors, which uses a variance-covariance matrix constructed from the residuals to account for arbitrary heteroscedasticity.  Here's what that gives you:


                    Estimate   Std. Error   t value      Pr(>|t|)
(Intercept)     2.319894e+00 3.954834e-02  58.65970  0.000000e+00
I(YEAR - 2000)  1.290703e-02 5.362464e-04  24.06922 5.252870e-128
log(Potential)  5.696738e-01 7.161458e-03  79.54718  0.000000e+00
gdd             1.931294e-04 5.058033e-06  38.18271  0.000000e+00
DD29           -2.477002e-02 1.397239e-03 -17.72783  2.557376e-70
Prec            1.786707e-02 1.099087e-03  16.25627  2.016306e-59
I(Prec^2)      -4.938967e-04 2.327153e-05 -21.22321 5.830391e-100
PDay           -6.798270e-03 7.381894e-05 -92.09386  0.000000e+00
DD29:AvgCO2     6.229397e-05 3.616307e-06  17.22585  1.698989e-66

The standard errors are larger and the T-values smaller, but this standard approach still gives us extraordinary confidence in our estimates.

You should remain skeptical.  Here's what happens when I use robust standard errors clustered by year:


                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     2.32e+00  5.57e-01  4.17 3.094e-05 ***
YEAR            1.29e-02  8.57e-03  1.52   0.12920    
log(Potential)  5.70e-01  9.11e-02  6.25 4.000e-10 ***
gdd             1.93e-04  7.89e-05  2.45   0.01443 *  
DD29           -2.48e-02  1.35e-02 -1.83   0.06719 .  
Prec            1.79e-02  1.06e-02  1.68   0.09243 .  
I(Prec^2)      -4.94e-04  2.15e-04 -2.29   0.02178 *  
PDay           -6.80e-03  8.17e-04 -8.32   2.2e-16 ***
DD29:AvgCO2     6.23e-05  3.50e-05  1.78   0.07510 .  


Standard errors are an order of magnitude larger and T-values are more humbling.  Planting date and potential yield come in very strong, but now everything else is just borderline significant.  It seems robust standard errors really aren't so robust.

But even if we cluster by year, we are probably missing some important dependence, since geographic regions may have similar errors across years, and in clustering by year, I assume all errors in one year are independent of all errors in other years.

If I cluster by state, the standard robust/clustering procedure will account for both geographic and time-series dependence within a state.  Since I know from earlier work that one state is about the extent of spatial correlation, this seems reasonable.  Here's what I get:

                  Estimate  Std. Error  t value  Pr(>|t|)    

(Intercept)     2.32e+00  1.1888e+00   1.9514 0.0510065 .  
YEAR            1.29e-02  4.6411e-03   2.7810 0.0054194 ** 
log(Potential)  5.70e-01  1.6938e-01   3.3632 0.0007706 ***
gdd             1.93e-04  2.2126e-04   0.8729 0.3827338    
DD29           -2.48e-02  2.6696e-02  -0.9279 0.3534781    
Prec            1.79e-02  1.2786e-02   1.3974 0.1622882    
I(Prec^2)      -4.94e-04  2.7371e-04  -1.8045 0.0711586 .  
PDay           -6.80e-03  4.9912e-04 -13.6205 < 2.2e-16 ***
DD29:AvgCO2     6.23e-05  6.8565e-05   0.9085 0.3635962    

Oops.  Now most of the weather variables have lost their statistical significance too.  But since I'm explicitly limiting assumed dependence in the cross section within years, now the time trend (YEAR) is significant, and it wasn't when clustering by YEAR.  We probably shouldn't take that significance very seriously, since some kinds of dependence (like technology) probably spans well beyond one state.

Note that this strategy of using large clusters combined with robust SE treatment (canned in STATA, for example) is what's recommended in Angrist and Pischke's Mostly Harmless Econometrics.

There are other ways of dealing with these kinds of problems.  For example, you can use a "block bootstrap" that resamples residuals whole years as a time, which preserves spatial correlation.  This is great in agricultural applications since weather is pretty much IID across years in a fixed locations and we should feel reasonably comfortable that there is little serial correlation.  One can also adapt the method by Conley for panel data.  Soloman Hsiang has graciously provided code here.  In earlier agriculture-related work, Wolfram Schlenker and I generally found that clustering by state gives similar standard errors as these methods.

The overarching lesson is: try it different ways and err on the side of least significance, because it's very easy to underestimate your standard errors and very hard to overestimate them.

And watch out for data errors: these have a way of screwing up both estimates and standard errors, sometimes quite dramatically.

If you had the patience to follow all of this, you might appreciate the footnotes and appendix in our recent comment on Deschenes and Greenstone.

Laboratory Grown Meat: The Next Green Revolution?

From what I've learned about agriculture over the last 10 years, I'm increasingly skeptical that we'll see another green revolution like the last one.  Crop yields for the major staples appear to be reaching agronomic limits in advanced nations.  While there's still room for improvement in developing nations, a lot of the low hanging fruit seems to have been picked.  And then their are challenges with climate change, which could be beneficial in some places, but likely damaging in most places, and possibly severely damaging.

So, where's a technological optimist to turn?

It seems to me that if we have another green revolution, it's going to look more like this. Right now a 5 oz hamburger, grown in a petri dish rather than scraped off a dead animal, costs a reported $325,000.  That's one expensive burger.  But it is easy to imagine how costs could come down in time.

Anyway, there's obviously a lot of uncertainty about this sort of thing, not the least of which is consumer acceptance.  But in the long run, this kind of technology might do a lot to feed a burgeoning planet in a way that's a lot less environmentally damaging, and depending on your point of view, more humane.

How farmers could benefit from fertilizer taxes

Some of the worst water quality problems result from nutrient leaching and runoff from agricultural lands.  Nitrogen and phosphorus applied to cropland and not absorbed by crops in the process of photosynthesis will, one way or another, one day or another, end up in the water.  The same goes for animal waste. The nutrients cause algae blooms, reduced concentrations of dissolved oxygen, and diminished fisheries and ecosystem health (called eutrophication). 

While there has been some effort to deal with these problems, I know of no great success stories, and water quality continues to decline in the Mississippi, the Gulf of Mexico, and the Chesapeake, the Great Lakes, and countless other water bodies.

One obvious remedy would be to tax fertilizer.  This would be a nearly Pigouvian solution.  Better would be to tax runoff and leaching directly, but that’s basically impossible for practical reasons.

The obvious but rarely stated problem is that it would probably require an extraordinarily large tax to have any real influence on the quantity of fertilizer used.  And politically powerful farmers would cry foul, which is why this kind of tax will probably never happen.

But I wonder: What would the incidence of a fertilizer tax, broadly applied, really be?  Agriculture is fairly competitive.  And demand for agricultural commodities is nearly vertical—about as inelastic as anything.  The econ 101 analysis would suggest that burden of the tax would fall mainly on consumers.  That is, food commodity prices would go up enough to compensate for most all of the tax.

Now, I’ve seen some economists propose fertilizer taxes on a graduated scale.  If fertilizer is applied at a sufficiently low rate, no tax would be levied, but the tax would then rise sharply with higher application levels (which is where most runoff and leaching comes from).  This would be a little harder to monitor, but probably not too bad.  If done this way, the total tax bill would cost farmers far less, but cause the same reduction in fertilizer use.  And farmers would still get the full compensating price increase, since less output would be collectively produced.

I think it’s possible—indeed, very probable—that the induced rise in commodity prices would more than compensate farmers for the fertilizer taxes they would have to pay under the graduated tax system.  That is, a statutory tax on farmers could cause their profits to go up.

Anyway, I don’t think anyone has made this point or emphasized it very well.  And it’s an important one, at least politically speaking, because maybe farmers could get on board with a tax that actually benefits them.  I’m not sure if it would save the Chesapeake Bay or Great Lakes from eutrophication, but I bet it would do a lot more good than anything else that’s been tried.

Update: Of course, this is no free lunch: consumers would pay in higher food prices.

Land Prices

Apologies for the radio silence.  At this point, I probably don't have many followers.  Life has been busy: a move to Hawaii, buying a home, adopting a child, a lot of teaching, new collaborations and meetings with a new joint appointment, editor and associate editor duties, and I'm probably juggling too many research projects.  It seems like research should be the first priority.  But it's so hard to get to it.  Thankfully, I have excellent collaborators and students to work with.

My colleagues over at G-FEED are doing a better job with regular postings.

Anyhow, there's a nice article in the New York Times about farmland prices.  The article suggests (as do most NYT articles on land values) that it's just another bubble.  Since we've had a couple bubbles in relatively recent memory, now everything's a bubble.

I'm not so sure.  There is a lot that's different about farmland values as compared to houses.  For one, we don't have quite the same level of debt relative to assets,  25.5%.  That's the highest since the 1980s boom.  But then given today's low interest rates, it's not really a fair comparison to the 1980s.  It's also less than half the 60%+ level of debt we had for housing at the peak of the bubble.  For another, we don't have stated-income mortgages.  Banks are more cautious with farms than they were with houses during the Countrywide Mortgage years.

The simple analysis compares the price to rent ratio to current long-run interest rates. Today it looks like land that sells for around $10,000/acre rents for $500-600/year.  That's a 5-6% yield where 10 year T-bill rates at this writing are a little under 2% and 30-year T-bill rates are a little over 3%.

Now, there are costs and benefits to owning land instead of T-bills.  A benefit to land is that is has built-in inflation protection.  Inflation will push up commodity and land prices like everything else. So, the better comparison may be real rates, which are currently negative for 10-20 years out, which makes a 5-6% yield look pretty good.

A cost to owning land is that it's risky.  Rents are high because commodity prices are high.  If commodity prices fall, so will rents and yield.  So, what's the forecast for prices?  I'd say that's hard to tell.  They could go up further, they might crash if rapid productivity growth resumes.  The best bet is to look at futures prices, which suggests prices will fall somewhat from today's levels, but not that much.  Then there is the question of how to price uncertainty around that best guess.

It's not clear how costly that risk really is.  For a family with all their wealth piled into the farm, the risk is a big deal.  Most family farms own their land outright, or have relatively small mortgages.  A downturn in commodity prices, rents and land values would hurt for sure.  But typically there's not the kind of leverage we had in homes (the Times article has a notable exception).

Consider, in contrast, the risk to outside investors.  Wall Street types are pilling into land, probably because they see a reasonable yield.  But their exposure to risk is quite different.  These are highly diversified investors who have large portfolios of assets.  If farmland values crash, it's unlikely to be closely associated with the rest of the economy.  The fact that agriculture is a small share of our economy, and that commodity prices have little association with the rest of the economy, means that risk is far less important to these outside investors than it is to the family farm.  This basic point was implicitly made by Gary Gorton and Greet Rouwenhorst awhile back.

So, the basic math suggest that land investment is a reasonable deal right now.  With prices this high, it may not be quite as good a deal as buying a home in some post-bubble cities with low price-to-rent ratios.  But it's not bad, and probably not as difficult for an arms-length investor to get into.

So, I don't think this is a bubble. However, you should be forewarned that I've been wrong about this kind of thing before.

Do Consumers Benefit from Energy Efficiency Regulations?

I have a new working paper with Xiaomei (Barbara) Chen, an NCSU graduate student who is on the job market this year, and Larry Dale and Hung-Chia Yang of Lawrence Berkeley National Laboratory.  LBL does a lot of work on energy efficiency regulations, and they asked us to do some data analysis to accompany new energy efficiency standards that were being proposed by the Department of Energy.  They had already done a lot of engineering analysis that typically goes along with these rules. But OMB was looking for an historical account of past standards, particularly the last time DOE increased standards for washers in 2007.  This paper comes out of that work.

This is a new area of research for me, and this paper could use some additional analysis if we can obtain the right data to do it. But even without that data, I think it presents some fairly compelling evidence that energy efficiency regulations may be beneficial in ways not typically considered

Here's the jist of it:  

We obtained a big dataset that tracks sales of all washing machines sold at a large fraction of retailers throughout the country.  These are proprietary data that LBL had to purchase, so unfortunately we cannot share the raw data.  They are monthly data that start well before a 2007 increase in the stringency of clothes washer efficiency standards and continue until well after 2007.  Importantly, the standards change only banned the manufacture, not the sale, of low efficiency washers. 

Unsurprisingly, we found prices of the banned low-efficiency units started increasing a bit before the ban took effect as supplies dwindled.  Price increases and falling quantities continued awhile after the ban. People who really wanted the old, less-efficient washers were clearly worse off because the old washers became more expensive and probably more difficult to find.  

As people shifted their purchases away from the banned units and toward the more efficient units, sales of the efficient units soared.   One may normally expect prices for high-efficiency units to also rise as demand shifted.  But they didn’t.  Instead prices fell sharply around the time of the policy change, just as sales started to rise.  Even if we look only at washers sold both before and after the policy change, the price declines of the efficient washers was larger the price increases of the less efficient washers. And while overall quality of washers increased, average prices declined.  Thus, not counting public and private benefits from energy saving, it seems pretty clear that consumers gained substantially from the policy change.

All of these effects become much clearer when we include washer model fixed effects to control changes in the mix of washers available on the market.

How could it be that regulation of the washer market—imposition of minimum efficiency standards—could cause average washer prices to fall?  The answer, we believe, goes back to a theoretical paper by Ronnen from 1991.  If there are economies of scale in production, as there are likely to be in most manufacturing (cost per unit declines as quantities scale up), then cost of producing efficient washers fell as demand and production increased. 

The question then becomes, why weren’t the more efficient washers simply sold on a larger scale before the policy change?  I think the likely answer to that is imperfect competition and associated price discrimination.  Appliance manufacturers develop a large variety of washers and try to carve out as many niche markets as possible, thereby obtaining some pricing power within each niche market.  These niche's become exaggerated if there is a lot of brand loyalty by consumers.  If there is too much variety, this can be very inefficient, especially if there are large economies of scale.  The end result is too many low-quality washers and too few high-quality washers.  When the policy banned low-efficiency units, it effectively banned low-quality washers and caused greater competition and economies of scale in the high-efficiency/high-quality market.

Note that I’m making a small leap by equating energy efficiency with quality, but the data generally seem to support this, as there is a strong association between price and efficiency, especially before the policy change. 

Now, one important caveat to this study is that other things were going on around the time of the policy change.  Some of the demand shift could have been driven by a spike in energy prices.  Also, the Great Recession began not long after the policy change, but I don't see how that could have caused a shift in demand toward higher-efficiency washers.  In both cases, since the substitution toward efficient washers began a bit before these nearly concurrent events, I think the fairest interpretation is that the policy change was the primary cause.  And even if the policy change wasn’t the cause, the pattern of price changes still suggests significant economies of scale and thus a potential role for policy given a monopolistically competitive market for appliances.

If our hypothesis is correct, we should be able to replicate the result for other appliances and standard changes.  We'll see…